darrylsj

Software going headless is inevitable in a world where agents use the tools 100X more than people do. And the reality is for a lot of software this is actually a huge boon to potential use-cases for these platforms. Software business models have largely been predicated on selling to the number of seats that are in the company in a given function, and the usage of your software is constrained by how much people can do in a given day. This means that your technology is often vastly underutilized relative to what it actually can power for the customer. Enter: agents. Agents can work 24/7, run in parallel, and string together work across systems. This is a big deal because now the agent can do far more than people ever could with these tools. Instead of reviewing contracts one by one, the agent will review all of them. Instead of manually moving data between marketing systems and across campaigns, the agent will let you run 10X more of them. Instead of being rate limited in a client onboarding process by human steps, agents accelerate these. Agents end up using these underlying platforms far more than people ever did, which opens up use-cases that the platform couldn’t go after before. Now, not every software market has the same amount of positive sum use-cases between people and agents, but I’d argue that a significant portion of systems of record, for instance, can be used far more than they are today. Your Salesforce data can be leveraged 100X more to do vastly more customer targeting and sales automation. Your documents can be turned into structured data and analyzed for insights and knowledge to automate other workflows. And so on. Now, of course you have to find a way to make this all commercially attractive, but it’s not hard to picture the revenue from API and agent consumption on these platforms becoming a rich component of revenue streams over time. Seats for the people, consumption for the agents. Lots of upside here for the companies that embrace this trend.

The idea that prompting would be useless is like if giving clear instructions to a brand new colleague who just joined your team is useless. “Prompting” should just encapsulate the entirely of giving the agent everything it needs to perform the task. This is high leverage.

As AI models get better at handling tools, and as context windows get bigger without as much rot, you can start to design agents more similar to how people work instead of having to mitigate the model limitations with weird hacks. For instance, even a year ago, if you were to build an agent to process large amounts of documents, the state of the art was to do embeddings on the data, then do a similarity search and pull out the chunks of content that matched (as well as surrounding chunks). This was necessary because context windows could only accurately handle a small amount of information at a time. This worked surprisingly well given the constraints (at least assuming you were working with authoritative data only), but had a lot of tricky limitations because it’s not how humans work. For instance, what do you do if the chunks you sent to the model were the most relevant semantically, but actually rendered irrelevant by some other part of the document. For instance, if at the top of the document it says “do not use this” but on page 3 there is information that’s relevant, that data will be sent to the model as the top hit. Similarly, chunked data is difficult when you need various parts of a document or many documents to be understood for answering a problem. Today, increasingly, you can begin to have agents effectively use tools and work with information far more similar to how people work. This unlocks a qualitatively different set of use-cases and capability level that agents can now handle. As we were designing the Box Agent, these improvements allowed us to rethink our entire architecture for AI. The agent can now search data similar to how a user searches, but with the benefit of being able to expand their queries, do semantic search, and process results nearly instantly. Then the agent can either read many documents at a time or at least much larger amounts of context. Again, much more similar to people, but now at hyperspeed. Importantly, beyond tool calling and context windows, the reasoning of models has also gone up enormously. This means the agent can also know when it needs to search for information again when it didn’t find something it was looking for or if something feels off. As model progress continues on the dimensions of context accuracy, tool calling, advanced reasoning, and coding, agents are going to become insanely powerful.

Introducing the new Box Agent. The Box Agent works across your entire Box file system, maintaining all your security and access controls, and is hyper tuned for working with enterprise content. This means you can now ask questions from all your enterprise content, search for files that were impossible to find before, deploy an agent on specific tasks on subsets of documents, analyze complex data sets, and generate or edit documents and spreadsheets via the agent. You can have the Box Agent search across your Box account to prepare for a sales meeting, analyze customer sentiment reports, process a large set of contracts for legal risk, provide insights into product development, leverage existing knowledge to answer RFPs, and thousands of other use-cases. 90% of enterprise data is unstructured data. This means most enterprise knowledge is sitting in inside of research reports, marketing assets, presentations, roadmap files, contracts, HR documents, and more. This is the critical context that agents need to be able to answer questions about a business, automate workflows, or serve up to other agents. We’ve been grinding on this for a quite a bit, and due to recent AI model advancements we’re now ready to release it to customers. Previous model generations had a difficult time knowing when to give up or keep going on a search, when to browse for files vs. use queries, how to rank files appropriately to know which version of content to use, how to handle large amounts of context to comb through, and more. Due to recent breakthroughs from models like GPT-5.4, Opus 4.6, and Gemini 3, we’ve seen major gains in tool calling, code execution, advanced reasoning, and more. Combined with an agent harness tuned to Box context, now it’s finally possible to have an agent that can work across your file system on long running tasks and actually deliver high quality results. Best of all, because the Box Agent works with any leading AI model, you’ll quickly get the gains coming out of the major labs as major new models are released. Further, openness at Box is key, so you’ll be able to call up the Box Agent from Box’s APIs and MCP server, so you can interact with Box intelligently from any other AI system. We know work happens everywhere, and we want to ensure you can access to the content you need from those places. The new Box Agent is available starting today, rolling out now for Enterprise Plus and Enterprise Advanced customers.

The worst thing you can do is just dabble with AI a *little bit*. That’s the spot where you use it and see its capability but over-generalize on the use cases and how easy the automation is. You almost have to use it too much, develop psychosis, then get to the other side and realize how much care and feeding and management of the agentic workflows is required. On that other end you realize you actually need to probably hire more (or new) people to then do all the new things agents can do.

The thing that most people miss initially with agents is that the scope of what we will produce will go up commensurate with what the tools can now automate, which basically means we’ll working the same or even more. Everyone thinks that we will use AI to do what we already do but cheaper and faster, which would lead to fewer people or getting more time back. In fact it will just mean we’re doing more things. Once we figured out that we can automate a particular task, you then expand the size of work to do many more of those or other tasks in a project. The result is that you’re actually combining many other previously hard to combine tasks into a single workflow, causing even more work. The software project scope now multiplies because you know you can build far more. The customer insights project now balloons because you know you can reasonably aggregate far more data. The marketing campaign has even more creative production because it’s cheaper and easier. This is going to happen in almost every field of work.

The official Box CLI is here. Now you can use Box via Claude Code, Codex, Perplexity Computer, OpenClaw & more as a full cloud file system for agents. Available to all users, including free users with 10GB of free storage. npm install --global @box/cli

Agent identities is going to be a super fun and hard problem for software in the coming years. Most agentic systems today assume that the agent can do everything the user can do, and just operate as an extension of that user. This has worked well and is how most auth has worked for cloud software, and it has made integrations super easy over time. But then comes along systems like Openclaw, and suddenly we get a new view into what becomes possible with agents that can operate on their own. And when you have many of them running in parallel. You start to work with them like a colleague, not just as an extension of what you do. But of course this now introduces an all new complexity than the traditional approach. What if you want an agent to access only a small subset of your data? What if you want an agent to have its own sandbox to operate in without any risk of a blast radius if it goes off the rails? What if you want to create an agent that can work with others, without you seeing everything it’s doing? For all of these cases, agents will start to need their own identities inside of platforms. To do this, we likely will need new mental models for how we delegate controls and access to them, how you handle authentication, who gets to manage them in an organization and so on. Lots to figure out in this space right now.